Age-related differences in physiology and a high prescription rate make the elderly more susceptible to ADEs.⁷ It is known that a number of common medications whose standard dosages for otherwise healthy adults are potentially harmful to older patients. Important untoward effects of medications include falls, hip fractures, intracranial hemorrhage, gastrointestinal bleeding, oversedation, and delirium, all of which result in lower quality of life and higher health service utilization.^8–16

Patients admitted to the hospital may be continued on medication regimens initiated in the ED. When a problem is discovered, it is often too late, and patients may experience adverse events, resulting in increased cost and length of stay.¹⁷ It is important to both the quality and safety of care for elderly patients that medication orders are initiated correctly in the ED. Traditional approaches for improving the appropriateness of medication use in the elderly have focused on avoiding a particular list or group of potentially inappropriate medications (PIMs). Beers was among the first to compile such a list. This list with subsequent modifications remains one of the more well-known guidelines of its type; it is widely used by government agencies and supported by researchers in various settings.^18–24 This approach has also been specifically recommended for geriatric care in the ED.²⁵ Application of Beers' criteria in the care of elderly outpatients suggests that nearly one third of certain medications prescribed may be inappropriate.^{26 27} A recent study estimates that PIMs are administered or prescribed in nearly 19.5 million or 16.8% of ED visits.²⁸ Similar estimates have been reported in Taiwan (20%),²⁹ Brazil (19.6%),³⁰ and Austria (12.5%).³¹ Data from one national study of ADEs in the ED suggest that simply avoiding PIMs from the Beers list may be insufficient in preventing ADEs.⁶ This study found that nearly 50% of all ADEs in the ED were attributable to three classes of medication (antiplatelets/anticoagulants, digoxin, and diabetic medications), and among the leading drugs in each of these classes, only one is considered a PIM. Conversely, these medications are generally considered critical for patients taking them. The ADEs related to these medications appear to be due to the narrow therapeutic windows of these medications, suggesting that the key for avoiding ADEs in many cases may be in improving dosing and frequency in prescribing rather than simply avoidance of PIMs.

Newer approaches to medication recommendations for elderly patients include consideration of alternate dosages and frequencies as well consideration of comorbidities and other patient factors in medication selection and dosing.³² Because few clinicians are expert in geriatric pharmacology, and medication orders in the ED often cannot be delayed for consultation, real-time decision support about drug selection and dosing would potentially be valuable to both clinicians and patients. Computerized physician order entry (CPOE) without added computerized decision support (CDS) has been shown to decrease serious medication errors, near misses or potential ADEs, and preventable ADEs.³³ Computerized decision support improves physician performance and clinical practice in roughly two-thirds of trials.^{34 35} When combined, CPOE and CDS have been shown to improve quality of care in both the inpatient^36–39 and the outpatient settings,^{40 41} and specifically in elderly inpatients.⁴² Taken together this suggests that computerized advice on ordering medications in the elderly ED patient might be an important additional preventive strategy.⁴³ Accordingly, CDS was among the highest priority recommendations made in a proposed research agendas for geriatric emergency medicine.^{44 45}

We found that providing age-related medication computerized decision support at the point of order entry resulted in increasing the proportion of medication orders consistent with recommendations. In addition, the ADE rate was lower in the intervention group, although several secondary outcomes did not change. Findings were primarily driven by opiates, representing approximately two-thirds of all potentially inappropriate orders. Findings for other medication categories were mixed, some with a potential learning (BZDs, sedative-hypnotics) and one inter-period potential reversal effect (sedative-hypnotic).

Our study design was based loosely on a similar inpatient study conducted in our institution, which obtained somewhat different results. That study found an improvement in the prescription of the recommended total daily dose, a reduction in the incidence of 10-fold dosing orders, and in the prescription of non-recommended drugs, and was associated with a lower in-hospital fall rate, but showed no reduction in hospital length of stay. Because the inpatient study had different primary and secondary outcome measures and used a different approach for the detection of ADEs reviewed, these are not directly comparable. Because patient length of stay in the ED is measured in hours rather than days, and since the majority of orders are for a frequency of ‘×1,’ the primary outcome measures focused on the initial order for a medication rather than a total daily dose received (as was the emphasis in the inpatient study). We restricted our analysis of agreement with recommendations to initial orders for a medication, excluding subsequent orders for the same drug. Such multiple orders were infrequent, and their inclusion would have complicated analysis and made explanation cumbersome without contributing to the analysis.

Our design, consisting of alternating OFF and ON periods of computerized decision support was intended to assess whether changes in ordering behavior across study periods were sustained or rather corresponded to the active status of the decision support tool. This would indicate whether potential changes were due to real time effects of decision support versus learning that took place after the initial activation period. Since residents rotate through the ED, and likely write the majority of medication orders (though this detail was not captured), this may have been unnecessary. In order to avoid problems associated with multiple testing, we did not perform a statistical test for each inter-period interval. However, there was a significant change across periods and an apparent trend present.

The decision support tool resulted in increased agreement of medication orders with recommendations, although overall agreement with recommendations was low, and the majority of medication substitution recommendations made were declined. This is likely the result of a number of factors. The first of these is that it is notoriously difficult to change physician behavior. Although computer interventions have fared well among various strategies for impacting behavior, this phenomenon continues to be a challenging area in research and quality improvement efforts.

We believe that perhaps a more important factor may be that the medication knowledge base used in our study was not designed specifically for the ED setting. Medication and dosing recommendations that are inconsistent with an emergency physician's experience regarding efficacy and safety in the acute setting, may lead to rejection of recommended changes. While it is unclear whether or to what extent input by emergency physicians would have changed recommendations for initial drug doses, especially for this first trial in the ED, it is possible that the low to moderate compliance rates might indicate general disagreement with some of these recommendations.

Emergency patients are often of higher acuity and in higher levels of pain than those in an outpatient clinic or perhaps the medical floor, and may in particular require higher doses of analgesia than required in other settings. In addition, since emergency physicians (EPs) are physically present in the treatment area when medications are ordered and administered to patients, it may be that the feedback EPs receive on the efficacy and the dangers of commonly used medications and dosages helps avoid the more frequent pitfalls in prescribing. In this light, it is not surprising that the most common reason given for overriding recommendations for an alternate medication was that the patient had not had problems with the drug previously.

These findings are also consistent with those of a recent study of the impact of CDS on prescribing behavior for outpatient prescriptions from the ED.⁴⁶ The knowledge base used in the present study shares similarities with the Beers' list, but as previously noted also includes recommendations for alternate dose, frequency, and total daily dose. It is encouraging that only 38 of the 85 unique medication/dosing recommendations were ordered in the ED. Limited applicability of existing medication recommendations for older adults to the emergency setting is not unique to our study, and has been previously discussed in the emergency medicine literature.^{25 51} It is worth noting that the development of Beers' criteria was consensus-based and that the original expert panel and subsequent updates have not included EP input. Recently, alternate criteria such as STOPP (Screening Tool of Older Persons' potentially inappropriate Prescriptions) have been proposed and studied.⁵² Further guideline development for use in the ED would likely benefit from site-specific adjustment.

We were interested in testing whether our intervention would result in fewer adverse events, recognizing that our study was potentially underpowered to detect this. We did find a decrease in ADEs during the periods when the CDS tool was active and that among the ADEs detected, the majority were related to medication orders not in agreement with recommendations. However, because the observed agreement rate with recommendations was low even during ON periods, this may limit the conclusions that can be drawn from this comparison.

As previously noted, because differences in ADE rates can be difficult to demonstrate, we looked at a number of secondary outcome measures intended as surrogates for adverse events, such as increased admission rate, EDLOS for discharged patients, use of rescue reversal drugs, and 10-fold drug orders. We did not find differences in these secondary outcome measures.

While the majority of recommendations from the knowledge base used in this study, in Beers' criteria, and other criteria proposed have focused on preventing PIMs or dosages thought to be associated with delirium, sedation, and other untoward outcomes, it bears consideration that there may also be risks and problems associated with under-treatment. For example, it is possible that recommendations erring toward subtherapeutic dosing of sedative-hypnotics in the setting of agitated delirium might result in an increased risk of self-harm or need for physical restraint use. Similarly, under-dosage of analgesics may result in under-treatment of pain and suffering. While the CDS knowledge base considered 5 mg of haloperidol to be a ‘10-fold order’ that might warrant review, many emergency physicians using this medication for acute agitation in the elderly might find 0.5 mg to be an ineffectively low starting dose. This reflects some of the limitations of the knowledge base which might be very appropriate for other settings or conditions but bear adjustment.

The decision-support tool and knowledge base could clearly be improved further. As noted, recommendations err on the side of conservative treatment, generally meaning alternate medications or lower dosages. Finding an appropriate range makes sense and recommendations that are sensitive to specific indications and to mental status would be ideal. It would also seem important to be able to assess the additive effects of medications of different types and among different classes. For example, if a patient were ordered a BZD and two different narcotic medications, the ordering physician would receive the same decision support as for a patient just receiving one of these three drugs. Future efforts could target such additional complexities and address errors of prescribing omission in the elderly,⁵³ and might include some educational intervention in addition to the CDS. Most immediately, future work should include derivation of an ED-specific evidence-based knowledge base for elderly medication dosing. Decision support using this knowledge base could then be re-examined to investigate its impact on elderly ED patients.